1. Field of the Invention
This invention relates generally to semiconductor fabrication technology, and, more particularly, to a method for semiconductor fabrication process monitoring and analysis.
2. Description of the Related Art
There is a constant drive within the semiconductor industry to increase the quality, reliability and throughput of integrated circuit devices, e.g., microprocessors, memory devices, and the like. This drive is fueled by consumer demands for higher quality computers and electronic devices that operate more reliably. These demands have resulted in a continual improvement in the manufacture of semiconductor devices, e.g., transistors, as well as in the manufacture of integrated circuit devices incorporating such transistors. Additionally, reducing defects in the manufacture of the components of a typical transistor also lowers the overall cost per transistor as well as the cost of integrated circuit devices incorporating such transistors.
The technologies underlying semiconductor processing tools have attracted increased attention over the last several years, resulting in substantial refinements. However, despite the advances made in this area, many of the processing tools that are currently commercially available suffer certain deficiencies. In particular, such tools often lack advanced process data monitoring capabilities, such as the ability to provide historical parametric data in a user-friendly format, as well as event logging, real-time graphical display of both current processing parameters and the processing parameters of the entire run, and remote, i.e., local site and worldwide, monitoring. These deficiencies can engender nonoptimal control of critical processing parameters, such as throughput accuracy, stability and repeatability, processing temperatures, mechanical tool parameters, and the like. This variability manifests itself as within-run disparities, run-to-run disparities and tool-to-tool disparities that can propagate into deviations in product quality and performance, whereas an ideal monitoring and diagnostics system for such tools would provide a means of monitoring this variability, as well as providing means for optimizing control of critical parameters.
Among the parameters it would be useful to monitor and control are critical dimensions (CDs) and doping levels for transistors (and other semiconductor devices), as well as overlay errors in photolithography. CDs are the smallest feature sizes that particular processing devices may be capable of producing. For example, the minimum widths w of polycrystalline (polysilicon or poly) gate lines for metal oxide semiconductor field effect transistors-(MOSFETs or MOS transistors) may correspond to one critical dimension (CD) for a semiconductor device having such transistors. Similarly, the junction depth dj (depth below the surface of a doped substrate to the bottom of a heavily doped source/drain region formed within the doped substrate) may be another critical dimension (CD) for a semiconductor device such as an MOS transistor. Doping levels may depend on dosages of ions implanted into the semiconductor devices, the dosages typically being given in numbers of ions per square centimeter at ion implant energies typically given in keV.
However, traditional statistical process control (SPC) techniques are often inadequate to control precisely CDs and doping levels in semiconductor and microelectronic device manufacturing so as to optimize device performance and yield. Typically, SPC techniques set a target value, and a spread about the target value, for the CDs, doping levels, and/or overlay errors in photolithography. The SPC techniques then attempt to minimize the deviation from the target value without automatically adjusting and adapting the respective target values to optimize the semiconductor device performance, as measured by wafer electrical test (WET) measurement characteristics, for example, and/or to optimize the semiconductor device yield and throughput. Furthermore, blindly minimizing non-adaptive processing spreads about target values may not increase processing yield and throughput.
Traditional control techniques are frequently ineffective in reducing off-target processing and in improving sort yields. For example, the wafer electrical test (WET) measurements are typically not performed on processed wafers until quite a long time after the wafers have been processed, sometimes not until weeks later. When one or more of the processing steps are producing resulting wafers that WET measurements indicate are unacceptable, causing the resulting wafers to be scrapped, this misprocessing goes undetected and uncorrected for quite a while, often for weeks, leading to many scrapped wafers, much wasted material and decreased overall throughput. Similarly, process and/or tool problems throughout the wafer processing are typically not analyzed fast enough, and final wafer yields are not evaluated on a die-by-die basis. Furthermore, data sets for making correlations between processing and/or tool trace data, on the one hand, and testing data, such as WET measurements, on the other, are typically manually extracted by the process engineers and put together, a very time-consuming procedure.
The present invention is directed to overcoming, or at least reducing the effects of, one or more of the problems set forth above.
In one aspect of the present invention, a method is provided for manufacturing, the method comprising processing a workpiece, measuring a parameter characteristic of the processing, and forming an output signal corresponding to the characteristic parameter measured by using the characteristic parameter measured as an input to a transistor model. The method also comprises predicting a wafer electrical test (WET) resulting value based on the output signal, detecting faulty processing based on the predicted WET resulting value, and correcting the faulty processing.
In another aspect of the present invention, a computer-readable, program storage device is provided, encoded with instructions that, when executed by a computer, perform a method for manufacturing a workpiece, the method comprising processing the workpiece, measuring a parameter characteristic of the processing, and forming an output signal corresponding to the characteristic parameter measured by using the characteristic parameter measured as an input to a transistor model. The method also comprises predicting a wafer electrical test (WET) resulting value based on the output signal, detecting faulty processing based on the predicted WET resulting value, and correcting the faulty processing.
In yet another aspect of the present invention, a computer programmed to perform a method of manufacturing is provided, the method comprising processing a workpiece, measuring a parameter characteristic of the processing, and forming an output signal corresponding to the characteristic parameter measured by using the characteristic parameter measured as an input to a transistor model. The method also comprises predicting a wafer electrical test (WET) resulting value based on the output signal, detecting faulty processing based on the predicted WET resulting value, and correcting the faulty processing.